In the fields of computational modeling and high performance computing, modeling platforms are known which contain a modeling engine to receive a variety of modeling inputs, and then generate a precise modeled output based on those inputs. In conventional modeling platforms, the set of inputs are precisely known, and the function applied to the modeling inputs is precisely known, but the ultimate results produced by the modeling engine are not known until the input data is supplied and the modeling engine is run. For example, in an econometric modeling platform, inputs for a particular industry like housing can be fed into a modeling engine. Those inputs can include, for instance, prevailing finance rates, employment rates, average new-home costs, costs of building materials, rate of inflation, and other economic or other variables that can be fed into the modeling engine which is programmed or configured to accept those inputs, apply a function or other processing to those inputs, and generate an output such as projected new-home sales for a given period of time. Those results can then be used to analyze or forecast other details related to the subject industry, such as predicted sector profits or employment.
In many real-life analytic applications, however, the necessary inputs for a given subject or study may not be known, while, at the same time, a desired or target output may be known or estimated with some accuracy. For instance, the research and development (R&D) department of a given corporation may be fixed at the beginning of a year or other budget cycle, but the assignment or allocation of that available amount of funds to different research teams or product areas may not be specified by managers or others. In such a case, an analyst may have to manually estimate and “back out” distributions of budget funds to different departments to begin to work out a set of component funding amounts that will, when combined, produce the already-known overall R&D or other budget. In performing that interpolation, the analyst may or may not be in possession of some departmental component budgets which have themselves also been fixed, or may or may not be in possession of the computation function which will appropriately sum or combine all component funds to produce the overall predetermined target budget. Adjustment of one component amount by hand may cause or suggest changes in other components in a ripple effect, which the analyst will then have to examine or account for in a further iteration of the same manual estimates.
In addition, in cases, after the interpolation results have been generated and presented to a user, it may be desirable or advantageous to permit the combination of various or multiple interpolation results or objects into a combined object. It may be the case, for example, that different sets of interpolation results, including interpolated input variables or values, may be generated at different times and/or by different users, so that different versions or dimensions of possibly related data may be developed and kept within an organization or otherwise. Similarly, in cases, a user may wish to join or “glue” related or unrelated sets of interpolated input data or other interpolation results to consolidate files, economize on storage, or to make aggregate interpolated data objects available to downstream applications that may wish to access that data from a unified location, such as spreadsheet, database, or modeling programs.
In these and other scenarios, it may be desirable to provide systems and methods for binding multiple interpolated data objects, in which a user can manipulate multiple interpolation data objects, generate data linkages between selected objects, and create an interpolation object consisting of two or more previously-generated interpolation outputs or results bound or linked into one combined object, for ease of access by other applications, flexibility of further interpolation activity, and to expand the scope of data that can be interpolated and/or seamlessly updated as a single data object, among other desirable goals.